Evaluating Bilingual Lexicon Induction without Lexical Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00142131" target="_blank" >RIV/00216224:14330/25:00142131 - isvavai.cz</a>

Result on the web

<a href="https://acl-bg.org/proceedings/2025/RANLP%202025/pdf/2025.ranlp-1.34.pdf" target="_blank" >https://acl-bg.org/proceedings/2025/RANLP%202025/pdf/2025.ranlp-1.34.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.26615/978-954-452-098-4-034" target="_blank" >10.26615/978-954-452-098-4-034</a>

Result language

angličtina

Original language name

Evaluating Bilingual Lexicon Induction without Lexical Data

Original language description

Bilingual Lexicon Induction (BLI) is a fundamental task in cross-lingual word embedding (CWE) evaluation, aimed at retrieving word translations from monolingual corpora in two languages. Despite the task’s central role, existing evaluation datasets based on lexical data often contain biases such as a lack of morphological diversity, frequency skew, semantic leakage, and overrepresentation of proper names, which undermine the validity of reported performance. In this paper, we propose a novel, language-agnostic evaluation methodology that entirely eliminates the dependency on lexical data. By training two sets of monolingual word embeddings (MWEs) using identical data and algorithms but with different weight initialisations, we enable the assessment on the BLI task without being affected by the quality of the evaluation dataset. We evaluate three baseline CWE models and analyse the impact of key hyperparameters. Our results provide a more reliable and bias-free perspective on CWE models’ performance.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing (RANLP)

ISBN

9789544520984

ISSN

2603-2813

e-ISSN

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Number of pages

8

Pages from-to

275-282

Publisher name

Incoma Ltd.

Place of publication

Varna, Bulgaria

Event location

Varna

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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